Component library retrieval using property models

The re-use of products such as code, specifications, design decisions and documentation has been proposed as a method for increasing software productivity and reliability. A major problem that has still to be adequately solved is the storage and retrieval of re-usable 'components'. Current methods, such as keyword retrieval and catalogues, rely on the use of names to describe components or categories. This is inadequate for all but a few well established components and categories; in the majority of cases names do not convey sufficient information on which to base a decision to retrieve. One approach to this problem is to describe components using a formal specification. However this is impractical for two reasons; firstly, the limitations of theorem proving would severely restrict the complexity of components that could be retrieved and secondly the retrieval mechanism would need to have a method of retrieving components with 'similar' specifications. This thesis proposes the use of formal 'property' models to represent the key functionality of components. Retrieval of components can then take place on the basis of a property model produced by the library's users. These models only describe the key properties of a component, thereby making the task of comparing properties feasible. Views are introduced as a method of relating similar, non identical property models, and the use of these views facilitates the re-use of components with similar properties. The language Miramod has been developed for the purpose of describing components, and a Miramod compiler and property prover which allow Miramod models to be compared for similarity, have been designed and implemented. These tools have indicated that model based component library retrieval is feasible at relatively low levels of the programming process, and future work is suggested to extend the method to encompass earlier stages in the development of large systems

Pattern-based refactoring in model-driven engineering

L’ingénierie dirigée par les modèles (IDM) est un paradigme du génie logiciel qui utilise les modèles comme concepts de premier ordre à partir desquels la validation, le code, les tests et la documentation sont dérivés. Ce paradigme met en jeu divers artefacts tels que les modèles, les méta-modèles ou les programmes de transformation des modèles. Dans un contexte industriel, ces artefacts sont de plus en plus complexes. En particulier, leur maintenance demande beaucoup de temps et de ressources. Afin de réduire la complexité des artefacts et le coût de leur maintenance, de nombreux chercheurs se sont intéressés au refactoring de ces artefacts pour améliorer leur qualité. Dans cette thèse, nous proposons d’étudier le refactoring dans l’IDM dans sa globalité, par son application à ces différents artefacts. Dans un premier temps, nous utilisons des patrons de conception spécifiques, comme une connaissance a priori, appliqués aux transformations de modèles comme un véhicule pour le refactoring. Nous procédons d’abord par une phase de détection des patrons de conception avec différentes formes et différents niveaux de complétude. Les occurrences détectées forment ainsi des opportunités de refactoring qui seront exploitées pour aboutir à des formes plus souhaitables et/ou plus complètes de ces patrons de conceptions. Dans le cas d’absence de connaissance a priori, comme les patrons de conception, nous proposons une approche basée sur la programmation génétique, pour apprendre des règles de transformations, capables de détecter des opportunités de refactoring et de les corriger. Comme alternative à la connaissance disponible a priori, l’approche utilise des exemples de paires d’artefacts d’avant et d’après le refactoring, pour ainsi apprendre les règles de refactoring. Nous illustrons cette approche sur le refactoring de modèles.Model-Driven Engineering (MDE) is a software engineering paradigm that uses models as first-class concepts from which validation, code, testing, and documentation are derived. This paradigm involves various artifacts such as models, meta-models, or model transformation programs. In an industrial context, these artifacts are increasingly complex. In particular, their maintenance is time and resources consuming. In order to reduce the complexity of artifacts and the cost of their maintenance, many researchers have been interested in refactoring these artifacts to improve their quality. In this thesis, we propose to study refactoring in MDE holistically, by its application to these different artifacts. First, we use specific design patterns, as an example of prior knowledge, applied to model transformations to enable refactoring. We first proceed with a detecting phase of design patterns, with different forms and levels of completeness. The detected occurrences thus form refactoring opportunities that will be exploited to implement more desirable and/or more complete forms of these design patterns. In the absence of prior knowledge, such as design patterns, we propose an approach based on genetic programming, to learn transformation rules, capable of detecting refactoring opportunities and correcting them. As an alternative to prior knowledge, our approach uses examples of pairs of artifacts before and after refactoring, in order to learn refactoring rules. We illustrate this approach on model refactoring

A Three-Phase Approach to Efficiently Transform C# into KDM

The Knowledge Discovery Metamodel (KDM) of the Object Management Group (OMG) is used in diverse research areas for describing software artifacts. It was recently adopted as standard ISO/IEC 19506 and its source, code, and action packages are highly suited for enabling language-independent source code analysis. However, a program needs to be transformed to KDM before corresponding source level metrics can be computed. To be of practical use, such a transformation (1) has to be resource-efficient and (2) ideally can be constructed on the basis of existing grammars to mitigate construction effort for a specific programming language. In this paper, we present such an efficient transformation for C# that is structured along three fundamental phases covering distinct sub-transformations for the types, members and methods, and statements. As our approach systematically analyzes and re-engineers existing grammars and integrates appropriate decompilers, it provides insights for fluently building those program transformations in general. Our quantitative evaluation uses three C# open source systems and an industrial software from the financial sector. It shows that our approach can be successfully applied to these systems and that the transformation can efficiently transform the programs to KDM while keeping resource demand low

Migrating Legacy Systems to Service-Oriented Architectures

This paper presents a methodology for migrating legacy systems towards Service-Oriented Architectures. The approach is based on source code analysis for identifying the contribution of code fragments to architectural elements and graph transformation for architectural migration, allowing for a high degree of automation. In order to transform existing application architectures into SOAs, the methodology has to be used in two dimensions, a technological and functional one. The work presented here is being developed in the context of a collaboration between academia and industry, and is aimed at being applied in real reengineering projects

Distributed Model-to-Model Transformation with ATL on MapReduce

International audienceEfficient processing of very large models is a key requirement for the adoption of Model-Driven Engineering (MDE) in some industrial contexts. One of the central operations in MDE is rule-based model transformation (MT). It is used to specify manipulation operations over structured data coming in the form of model graphs. However, being based on com-putationally expensive operations like subgraph isomorphism, MT tools are facing issues on both memory occupancy and execution time while dealing with the increasing model size and complexity. One way to overcome these issues is to exploit the wide availability of distributed clusters in the Cloud for the distributed execution of MT. In this paper, we propose an approach to automatically distribute the execution of model transformations written in a popular MT language, ATL, on top of a well-known distributed programming model, MapReduce. We show how the execution semantics of ATL can be aligned with the MapReduce computation model. We describe the extensions to the ATL transformation engine to enable distribution, and we experimentally demonstrate the scalability of this solution in a reverse-engineering scenario

Automated analysis of feature models: Quo vadis?

Feature models have been used since the 90's to describe software product lines as a way of reusing common parts in a family of software systems. In 2010, a systematic literature review was published summarizing the advances and settling the basis of the area of Automated Analysis of Feature Models (AAFM). From then on, different studies have applied the AAFM in different domains. In this paper, we provide an overview of the evolution of this field since 2010 by performing a systematic mapping study considering 423 primary sources. We found six different variability facets where the AAFM is being applied that define the tendencies: product configuration and derivation; testing and evolution; reverse engineering; multi-model variability-analysis; variability modelling and variability-intensive systems. We also confirmed that there is a lack of industrial evidence in most of the cases. Finally, we present where and when the papers have been published and who are the authors and institutions that are contributing to the field. We observed that the maturity is proven by the increment in the number of journals published along the years as well as the diversity of conferences and workshops where papers are published. We also suggest some synergies with other areas such as cloud or mobile computing among others that can motivate further research in the future.Ministerio de Economía y Competitividad TIN2015-70560-RJunta de Andalucía TIC-186

Model Transformation Languages with Modular Information Hiding

Model transformations, together with models, form the principal artifacts in model-driven software development. Industrial practitioners report that transformations on larger models quickly get sufficiently large and complex themselves. To alleviate entailed maintenance efforts, this thesis presents a modularity concept with explicit interfaces, complemented by software visualization and clustering techniques. All three approaches are tailored to the specific needs of the transformation domain

On the engineering of crucial software

The various aspects of the conventional software development cycle are examined. This cycle was the basis of the augmented approach contained in the original grant proposal. This cycle was found inadequate for crucial software development, and the justification for this opinion is presented. Several possible enhancements to the conventional software cycle are discussed. Software fault tolerance, a possible enhancement of major importance, is discussed separately. Formal verification using mathematical proof is considered. Automatic programming is a radical alternative to the conventional cycle and is discussed. Recommendations for a comprehensive approach are presented, and various experiments which could be conducted in AIRLAB are described

A Unified Format for Language Documents

We have analyzed a substantial number of language documentation artifacts, including language standards, language specifications, language reference manuals, as well as internal documents of standardization bodies. We have reverse-engineered their intended internal structure, and compared the results. The Language Document Format (LDF), was developed to specifically support the documentation domain. We have also integrated LDF into an engineering discipline for language documents including tool support, for example, for rendering language documents, extracting grammars and samples, and migrating existing documents into LDF. The definition of LDF, tool support for LDF, and LDF applications are freely available through SourceForge